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题名: 北京城市居民区植被格局及其滞尘效应研究
作者: 颜景理
学位类别: 博士
答辩日期: 2016-05
授予单位: 中国科学院研究生院
授予地点: 北京
导师: 周伟奇
关键词: 城市植被生态系统 ; Urban vegetation ecosystem ; 居民区植被景观 ; Residential vegtation landscape ; 滞留颗粒物 ; Particle retention ; 生态系统服务,面 向对象分类 ; Ecosystem service ; Object-based classification
其他题名: Quantifying the fine-scale spatiotemporal pattern of vegetation cover and its dust retention in Beijing urban residential areas
学位专业: 生态学
中文摘要:     快速城市化导致的生态环境恶化,尤其是空气污染,是我国许多城市健康发展所面临的主要的问题。城市植被对城市大气环境具有明显的改善作用,并且不同的植被结构组成和格局配置具有显著不同的生态环境效应。因此,分析精细尺度上的城市植被格局特征及其生态环境效应,并据此优化城市植被空间格局配置,提高其生态系统服务,是改善城市生态环境的重要途径之一。
    本文选择北京市五环内区域为研究区,定量分析了城市精细尺度植被格局,并评价其滞尘效应,包括三个主要研究内容:1)城市精细尺度的植被格局定量方法,基于多时相 WorldView-2影像数据,提出了一种结合植被物候和面向对象分类的城市植被类型分类方法;2)居民区植被景观格局特征与驱动机制分析,选择北京市五环内居住小区为研究对象,分析居民区植被景观格局特征及差异,并结合城市典型社会经济因子,分析居民区植被景观格局特征的主要影响因素;3)城市植被滞尘效应估算,研究首先构建了一种采用面向对象图像分析技术,利用扫描电镜照片,快速定量植被叶面颗粒物的新方法,然后结合实地调查数据和遥感反演方法估算了北京市五环内植被滞尘量。主要结论如下:
    1.应用植被物候信息能够显著提高城市植被分类精度。通过采用多时相WorldView-2影像获得植被物候信息并用于分类,总体分类精度由单景影像的 82%提高到90%。落叶树、针叶树和阴影植被的精度分别提高了 8.77%、9.14%和14.95%。研究显示 WorldView-2影像新增波段中红边和近红外这  2个波段在植被类型识别中的作用高于传统波段。
    2.北京市五环内居民区基本特征。北京五环内居民区总面积达222.92 km2,约占五环内面积的 33.25%。居民区植被总面积为  49.6km2,占居民区总面积的22.24%。五环内居民区建筑以多层建筑为主,占总数的 46.99%;建成年代为1998-2004年间的居民区占总数的  47.83%。
    3.居民区植被景观格局特征。居民区平均植被覆盖比例为22.04%,其中二至三环内居民区植被覆盖比例最高,为 23.68%,二环内最低,为 21.02%。同时,居民区植被覆盖比例与建成年份和建筑高度都呈负相关关系。植被空间配置上,二环内和南三环外居民区植被多是分散型小斑块,二至三环内和北三环外呈现少量大面积斑块辅以大量小面积斑块的景观格局。
    4.影响居民区植被景观的社会经济因素。居民区建成年份是影响居民区植被景观组成和配置的最主要因素,与植被覆盖比例和格局配置成负相关。其次是建筑高度,影响植被斑块边缘形状和斑块数量,并呈现负相关。居民区单位价格和距市中心距离与植被斑块边缘形状正相关。
    5.不同物种间的滞尘差异显著。黄刺玫的单位面积滞尘能力显著高于构树和白皮松,同时黄刺玫滞留细颗粒(直径≤2.5μm)更有效率。超过 90%的颗粒物直径≤10μm,54.8%的颗粒物直径≤2.5μm。86.4-93.8%的颗粒物边界较平滑,23.4%的颗粒物近圆形。
    6.北京城市区域植被滞尘效应。北京市五环内植被2009年滞尘总量为6124.16吨,其中居民区植被滞尘量为1821.12吨,占五环内植被滞尘总量的29.74%,略高于五环内居民区植被覆盖面积比例(22.24%)。
    7.北京城市植被滞尘空间特征。北京五环内不同区域植被滞尘差异明显,西北和东南部公园绿地具有最高的单位面积滞尘量。植被物候阶段显著影响植被滞尘能力,植被生长季和非生长季的滞尘总量相差3.45倍。叶面积指数是影响遥感反演植被滞尘效应的主要因素。
英文摘要:     Many cities  in  China, like  Beijing,  are facing  severe environmental  problems,which have  great impacts  on life quality  and human  health of urban  citizens. Urban greenspace  can provide  multiple ecosystem  services  to significantly  improve urban environment. The provision of these services, however, can be affected by the structure and spatial  distribution  of urban  vegetation.  To achieve  the goal  of  maximizing its provision of ecosystem services by optimizing spatial pattern of urban vegetation,  it is crucial  to first  quantify  the  spatial pattern  of  urban  vegetation,  and understand  its linkage to ecosystem service provision.
    The overall objective  of this study is  to understand the pattern of  green cover in residential landscapes of Beijing, and its ecosystem services in fine particulate material retention. Focusing on  the areas within  the 5th-ring road, this  study 1) quantified the fine-scale  vegetation   pattern.  We   test  a   new  method   by  integrating   vegetation phenology and object-based classification; 2) examined the driving forces of residential
vegetation   landscape.  We   first   quantified   the  distribution   of   urban   residential communities,  and  then  analyzed  the  main  driving  forces  of  landscape  pattern  of residential vegetation combining typical social-economic factors; and 3) evaluated fine particulate  material retention  provided by  urban  plants. An  innovative  method was developed   by   integrating   object-based   image   analysis    and   scanning   electron
microscope  photos,  and   we  then  calculated   particle  retention  amount  for   urban vegetation by employing field investigation and remote sensing techniques. We found:
    1. The integration of  vegetation phenology into object-based classification  could greatly improve the classification accuracy  of identifying urban vegetation types. The overall accuracies were improved from 82% to 90%, with accuracy improved by 14.95% for shaded vegetation, 9.14% for evergreen trees, 8.77% for broadleaf trees, and 5% for grass/lawn. It showed that  the two new additional bands,  that is, Red edge and NIR2,were  more important  in  classifying vegetation  types than  the  other  four traditional bands.
   2. The  residential landscapes covers  an area of  222.92 km2, 33.25% of  the 5th-ring road area. Vegeation cover in the residential landscapes has a total area of 49.6 km2,or 22.24%  of the  total residential  areas. Multi-rise  buildings  are the  most dominant bulding type, accounting for 46.99% of all the  buildings. Approximately 47.8% of the residential communities were built between 1998 and 2004. With respect to vegetation pattern, small-sized  patches were  scattered in  communities  within the  2th ring  road areas,  as  well  as  the  regions  outside of  the  south  3th  ring  road.  Meanwhile,  the communities between 2th and 3th  ring roads, and out of the north of the  3th ring road were dominanted with the mixture of a few large patches and many small-sized patches.
   3. The proportional  cover of vegetation  was negatively correlated  with building age and building height. In fact, building age was the most correlative social-economic factors  for  landscape  pattern and  configuration  of  residential  vegetation.  Building height was related to the  shape of vegetation patches and number of patches. Housing price and distance to city center were marginally related to vegetation landscape.
    5. The particle retention efficiency of  Rosa xanthina Lindl.(RL) was greater than that of Broussonetia Papyrifera (BP) and Pinus bungeana Zucc.(PZ). And RL was more efficient  in  capturing   fine  particles  with  diameter≤2.5µm,   while  PZ  at  particles diameter≥10µm. The majority of the particles (from 95.7% of PZ to 99.1% of BP) have diameter≤10µm, and 54.8% were≤2.5µm; most particles, from  86.4% of RL to 93.8% of  BP,  had  smooth  edges  with  border  index≤1.4,  and  23.4%  of  the particles  are approximately spherical shape (compactness≥0.84).
    6. The total amount of particles captured by urban plants for 2009 in the five rings areas was 6124.16t,  and 29.74% of them,  1821.12t, were from residential  vegetation.The capacity of  particle retention varied by locations,  where park greenspace had  the highest ability in particle capture per unit area.
    7. Vegetation  phenology can  significantly affect  particle retention capacity  (3.5 time differences  in different  phenologies). Therefore,  it is  important to  condiser the effects of vegetation phenology in particle retention estimation. In addition, the spatial resolution of remote sensing image affected the estimation of particle retention, as leaf area index was the  major factor influencing particle capturing, and  it is highly related to spatial rsolution.
内容类型: 学位论文
URI标识: http://ir.rcees.ac.cn/handle/311016/37033
Appears in Collections:城市与区域生态国家重点实验室_学位论文

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Recommended Citation:
颜景理. 北京城市居民区植被格局及其滞尘效应研究[D]. 北京. 中国科学院研究生院. 2016.
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